Towards closing the energy gap between HOG and CNN features for embedded vision (Invited paper)

@article{Suleiman2017TowardsCT,
  title={Towards closing the energy gap between HOG and CNN features for embedded vision (Invited paper)},
  author={Amr Suleiman and Yu-Hsin Chen and Joel S. Emer and Vivienne Sze},
  journal={2017 IEEE International Symposium on Circuits and Systems (ISCAS)},
  year={2017},
  pages={1-4}
}
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy and/or latency concerns. Accordingly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial. While deep learning is gaining popularity in several computer vision algorithms, a significant energy consumption difference… CONTINUE READING
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